Adaptive margin slack minimization in RKHS for classification
Paper i proceeding, 2016

In this paper, we design a novel regularized empirical risk minimization technique for classification called Adaptive Margin Slack Minimization (AMSM). The proposed method is based on minimizing a regularized upper bound of the misclassification error. Compared to the cost function of the classical L2-SVM, AMSM can be interpreted as minimizing a tighter bound with some additional flexibilities regarding the choice of marginal hyperplane. A hyperparameter-free adaptive algorithm is presented for finding a solution to the proposed risk function. Numerical results shows that AMSM outperforms L2-SVM on the tested standard datasets.

Reproducing Kernel Hilbert Space

Structural Risk Minimization

Adaptive Margin

L2-SVM

Författare

Yinan Yu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Konstantinos I. Diamantaras

Chalmers University of Technology

TEI of Thessaloniki

Princeton University

Tomas McKelvey

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

S. Y. Kung

TEI of Thessaloniki

Chalmers University of Technology

Princeton University

41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20-25 March 2016

1520-6149 (ISSN)

2319-2323

Ämneskategorier

Signalbehandling

DOI

10.1109/ICASSP.2016.7472091

ISBN

978-1-4799-9988-0